Bayesian Parameter Estimation via Filtering and Functional Approximations
Type
PreprintKAUST Department
Extreme Computing Research CenterComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Date
2016-11-25Permanent link to this record
http://hdl.handle.net/10754/626468
Metadata
Show full item recordAbstract
The inverse problem of determining parameters in a model by comparing some output of the model with observations is addressed. This is a description for what hat to be done to use the Gauss-Markov-Kalman filter for the Bayesian estimation and updating of parameters in a computational model. This is a filter acting on random variables, and while its Monte Carlo variant --- the Ensemble Kalman Filter (EnKF) --- is fairly straightforward, we subsequently only sketch its implementation with the help of functional representations.Publisher
arXivarXiv
1611.09293Additional Links
http://arxiv.org/abs/1611.09293v1http://arxiv.org/pdf/1611.09293v1